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Found 6 papers, 2 papers with code
Large Language Model with Region-guided Referring and Grounding for CT Report Generation
It leverages the recognition of referring regions to guide the generation of region-specific reports, enhancing the model's referring and grounding capabilities while also improving the report's interpretability.
GSCo: Towards Generalizable AI in Medicine via Generalist-Specialist Collaboration
Specifically, we propose a cooperative framework, Generalist-Specialist Collaboration (GSCo), which consists of two stages, namely the construction of GFM and specialists, and collaborative inference on downstream tasks.
Dia-LLaMA: Towards Large Language Model-driven CT Report Generation
Medical report generation has achieved remarkable advancements yet has still been faced with several challenges.
XCoOp: Explainable Prompt Learning for Computer-Aided Diagnosis via Concept-guided Context Optimization
Utilizing potent representations of the large vision-language models (VLMs) to accomplish various downstream tasks has attracted increasing attention.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Iterative Semi-Supervised Learning for Abdominal Organs and Tumor Segmentation
Initially, a deep model (nn-UNet) trained on datasets with complete organ annotations (about 220 scans) generates pseudo labels for the whole dataset.
ECNU at SemEval-2020 Task 7: Assessing Humor in Edited News Headlines Using BiLSTM with Attention
In this paper we describe our system submitted to SemEval 2020 Task 7: {``}Assessing Humor in Edited News Headlines{''}.
